Basic Registration

Summary:

1. Creating an instance of the registration framework requires selection of the following components:
   • Optimizer.
   • Similarity metric.
   • Interpolator.

2. The registration framework only supports images with sitkFloat32 and sitkFloat64 pixel types (use the SimpleITK Cast() function if your image's pixel type is something else).

3. Successful registration is highly dependent on initialization. In general you can:

   • Use auxiliary information or user interaction to obtain an initial transformation (avoid resampling).
   • Center the images using the CenteredTransformInitializer.
   • Coarsely sample the parameter space using the Exhaustive Optimizer to obtain one or more initial transformation estimates.
   • Manually initialize, via direct manipulation of transformation parameters and visualization or localization of corresponding points in the two images and then use the LandmarkBasedTransformInitializer.

Registration Components

There are many options for creating an instance of the registration framework, all of which are configured in SimpleITK via methods of the ImageRegistrationMethod class. This class encapsulates many of the components available in ITK for constructing a registration instance.

Currently, the available choices from the following groups of ITK components are:

Optimizers

The SimpleITK registration framework supports several optimizer types via the SetOptimizerAsX() methods, these include:

• Exhaustive
• Nelder-Mead downhill simplex, a.k.a. Amoeba.
• Powell optimizer.
• 1+1 evolutionary optimizer.
• Variations on gradient descent:
  • GradientDescent
  • GradientDescentLineSearch
  • RegularStepGradientDescent
• ConjugateGradientLineSearch
• L-BFGS2 (Limited memory Broyden, Fletcher,Goldfarb,Shannon)
• L-BFGS-B (Limited memory Broyden, Fletcher,Goldfarb,Shannon-Bound Constrained) - supports the use of simple constraints ($l\leq x \leq u$)

Similarity metrics

The SimpleITK registration framework supports several metric types via the SetMetricAsX() methods, these include:

• MeanSquares
• Demons
• Correlation
• ANTSNeighborhoodCorrelation
• JointHistogramMutualInformation
• MattesMutualInformation

Interpolators

The SimpleITK registration framework supports several interpolators via the SetInterpolator() method, which receives one of the following enumerations:

• sitkNearestNeighbor
• sitkLinear
• sitkBSpline
• sitkGaussian
• sitkHammingWindowedSinc
• sitkCosineWindowedSinc
• sitkWelchWindowedSinc
• sitkLanczosWindowedSinc
• sitkBlackmanWindowedSinc

import SimpleITK as sitk
from downloaddata import fetch_data as fdata
%matplotlib notebook
import gui
import registration_gui as rgui
import matplotlib.pyplot as plt

import numpy as np
import os
OUTPUT_DIR = 'output'


import numpy as np
from ipywidgets import interact, fixed

Read images

We first read the images, specifying the pixel type that is required for registration (Float32 or Float64) and look at them. In this notebook we use a CT and MR image from the same patient. These are part of the training data from the Retrospective Image Registration Evaluation (RIRE) project.

#fetching COVID dicom data from directory
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames('../../MIDRC-RICORD-1A/MIDRC-RICORD-1A-419639-000082/08-02-2002-CT CHEST WITHOUT CONTRAST-04614/601.000000-COR 3X3-86740/')
reader.SetFileNames(dicom_names)

fixed_image = reader.Execute()

dicom_names = reader.GetGDCMSeriesFileNames('../../MIDRC-RICORD-1A/MIDRC-RICORD-1A-419639-000082/08-02-2002-CT CHEST WITHOUT CONTRAST-04614/604.000000-COR 3X3-11320/')
reader.SetFileNames(dicom_names)

moving_image = reader.Execute()
#done


#Casting images from 32-bit integer to 32-bit float
fixed_image = sitk.Cast(fixed_image, sitk.sitkFloat32)
moving_image = sitk.Cast(moving_image, sitk.sitkFloat32)

#resampledImage = sitk.Resample(img2_original, img1)


gui.MultiImageDisplay(image_list = [fixed_image, moving_image],                   
                      title_list = ['Fixed Image', 'Moving Image'],
                      figure_size=(8,5));
#this image intensity is much clearer and effective for using the CenteredTransformInitializerFilter 

intensity_profile_image = moving_image
fig = plt.figure()
plt.hist(sitk.GetArrayViewFromImage(intensity_profile_image).flatten(),bins=100);

intensity_profile_image = fixed_image
plt.hist(sitk.GetArrayViewFromImage(intensity_profile_image).flatten(),bins=100);

Classic Registration

Estimate a 3D rigid transformation between images of different modalities.

We have made the following choices with respect to initialization and registration component settings:

• Similarity metric, mutual information (Mattes MI):
  • Number of histogram bins, 50.
  • Sampling strategy, random.
  • Sampling percentage, 1%.
• Interpolator, sitkLinear.
• Optimizer, gradient descent:
  • Learning rate, step size along traversal direction in parameter space, 1.0 .
  • Number of iterations, maximal number of iterations, 100.
  • Convergence minimum value, value used for convergence checking in conjunction with the energy profile of the similarity metric that is estimated in the given window size, 1e-6.
  • Convergence window size, number of values of the similarity metric which are used to estimate the energy profile of the similarity metric, 10.

We initialize registration by aligning the centers of the two volumes. To qualitatively evaluate the result we use a linked cursor approach, click on one image and the corresponding point is added to the other image.

#this allows us to map points to see 
initial_transform = sitk.CenteredTransformInitializer(fixed_image, 
                                                      moving_image, 
                                                      sitk.Euler3DTransform(), 
                                                      sitk.CenteredTransformInitializerFilter.GEOMETRY)

gui.RegistrationPointDataAquisition(fixed_image, moving_image, figure_size=(9,4), known_transformation=initial_transform);

Before Registration: misalignment between fixed/moving image is obvious

Run the next cell three times:

1. As is.
2. Uncomment the automated optimizer scale setting so that a change in rotation (radians) has a similar effect to a change in translation (mm).
3. Uncomment the multi-resolution settings.

registration_method = sitk.ImageRegistrationMethod()

# Similarity metric settings.
registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)
registration_method.SetMetricSamplingPercentage(0.01)

#Interpolator
registration_method.SetInterpolator(sitk.sitkLinear)

# Optimizer settings.
registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100, convergenceMinimumValue=1e-6, convergenceWindowSize=10)
registration_method.SetOptimizerScalesFromPhysicalShift()
#uncomment line above, it makes all the differnce

# Don't optimize in-place, we would possibly like to run this cell multiple times.
registration_method.SetInitialTransform(initial_transform, inPlace=False)

# Connect all of the observers so that we can perform plotting during registration.
registration_method.AddCommand(sitk.sitkStartEvent, rgui.start_plot)
registration_method.AddCommand(sitk.sitkEndEvent, rgui.end_plot)
registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, rgui.update_multires_iterations) 
registration_method.AddCommand(sitk.sitkIterationEvent, lambda: rgui.plot_values(registration_method))

final_transform = registration_method.Execute(fixed_image, moving_image)

# Always check the reason optimization terminated.
print('Final metric value: {0}'.format(registration_method.GetMetricValue()))
print('Optimizer\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))

Final metric value: -1.091190129503746
Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 54.

Qualitatively evaluate the result using a linked cursor approach (visual evaluation):

gui.RegistrationPointDataAquisition(fixed_image, moving_image, figure_size=(8,4), known_transformation=final_transform);

After Registration: significant improvement in alignment

If we are satisfied with the results, save them to file.

moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform, sitk.sitkLinear, 0.0, moving_image.GetPixelID())
sitk.WriteImage(moving_resampled, os.path.join(OUTPUT_DIR, 'RIRE_training_001_mr_T1_resampled.mha'))
sitk.WriteTransform(final_transform, os.path.join(OUTPUT_DIR, 'RIRE_training_001_CT_2_mr_T1.tfm')) 

#fetching COVID dicom data from directory
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames('../../MIDRC-RICORD-1A/MIDRC-RICORD-1A-419639-000082/08-02-2002-CT CHEST WITHOUT CONTRAST-04614/601.000000-COR 3X3-86740/')
reader.SetFileNames(dicom_names)

fixed_image = reader.Execute()

dicom_names = reader.GetGDCMSeriesFileNames('../../MIDRC-RICORD-1A/MIDRC-RICORD-1A-419639-000082/08-02-2002-CT CHEST WITHOUT CONTRAST-04614/604.000000-COR 3X3-11320/')
reader.SetFileNames(dicom_names)

moving_image = reader.Execute()

#cast imported images to floats
#Most often images have a high dynamic range. Thus, to write them to file in a format appropriate 
#for use in a manuscript we will need to map the intensities to a low dynamic range (e.g. [0,255])
#In SimpleITK this is readily done with the IntensityWindowingImageFilter.
fixed_image = sitk.Cast(sitk.IntensityWindowing(fixed_image), sitk.sitkFloat32)
moving_image = sitk.Cast(sitk.IntensityWindowing(moving_image), sitk.sitkFloat32)

#changing the window level 
ct_window_level = [835,230]
mr_window_level = [635,162]


gui.MultiImageDisplay(image_list = [fixed_image, moving_image],                   
                      title_list = ['fixed', 'moving'], figure_size=(8,4), window_level_list=[ct_window_level, mr_window_level]);

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Identity transform initialization